---
title: "Dashboard of Manhattan Restaurant Inspection in 2022"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(p8105.datasets)
library(plotly)
library(httr)
library(jsonlite)
library(stringr)
library(lubridate)
```

```{r, include = FALSE}
get_all_inspections = function(url) {
  
  all_inspections = vector("list", length = 0)
  
  loop_index = 1
  chunk_size = 50000
  DO_NEXT = TRUE
  
  while (DO_NEXT) {
    message("Getting data, page ", loop_index)
    
    all_inspections[[loop_index]] = 
      GET(url,
          query = list(`$order` = "zipcode",
                       `$limit` = chunk_size,
                       `$offset` = as.integer((loop_index - 1) * chunk_size)
                       )
          ) %>%
      content("text") %>%
      fromJSON() %>%
      as_tibble()
    
    DO_NEXT = dim(all_inspections[[loop_index]])[1] == chunk_size
    loop_index = loop_index + 1
  }
  
  all_inspections
  
}

url = "https://data.cityofnewyork.us/resource/43nn-pn8j.json"

nyc_inspections = 
  get_all_inspections(url) %>%
  bind_rows() %>%
  filter(
    boro == "Manhattan",
    !is.na(score)) %>%
  select(camis, boro, zipcode, critical_flag, dba, violation_code, violation_description, score, grade, cuisine_description, inspection_date, latitude, longitude) %>%
  mutate_at("inspection_date", str_replace, "T00:00:00.000", "") %>%
  mutate(
    score = as.numeric(score),
    zipcode = as.factor(zipcode),
    grade = as.factor(grade),
    critical_flag = as.factor(critical_flag),
    inspection_date = as.Date(inspection_date),
    cuisine_description = as.factor(cuisine_description),
    violation_code = as.factor(violation_code)
  ) %>%
  filter(inspection_date > ymd(20220101))
```

Column {data-width=650}
-----------------------------------------------------------------------

### The Scatterplot

```{r}
nyc_inspections %>%
  select(latitude, longitude, grade) %>%
  filter(
    !is.na(latitude),
    !is.na(longitude),
    !is.na(grade),
    latitude != 0,
    longitude != 0,
    grade == "A" | grade == "B" | grade == "C"
  ) %>%
  mutate(text_label = str_c("Grade: ", grade),
         latitude = round(as.numeric(latitude), digits = 5),
         longitude = round(as.numeric(longitude), digits = 5)) %>% 
  plot_ly(
    x = ~latitude, 
    y = ~longitude, 
    type = "scatter", 
    mode = "markers",
    color = ~grade, 
    text = ~text_label, 
    colors = "viridis",
    alpha = 0.1)
```

Column {data-width=350}
-----------------------------------------------------------------------

### The Barplot

```{r}
nyc_inspections %>% 
  filter(zipcode == "10032" | zipcode == "10033" | zipcode == "10040") %>%
  count(cuisine_description) %>% 
  mutate(cuisine_description = fct_reorder(cuisine_description, n)) %>% 
  plot_ly(x = ~cuisine_description, y = ~n, color = ~cuisine_description, type = "bar", colors = "viridis")
```

### The Boxplot

```{r}
nyc_inspections %>% 
  filter(
    zipcode == "10032" | zipcode == "10033" | zipcode == "10040",
    cuisine_description != "Bakery Products/Desserts" & 
      cuisine_description != "Donuts" &
      cuisine_description != "Frozen Desserts" &
      cuisine_description != "Juice, Smoothies, Fruit Salads" &
      cuisine_description != "Coffee/Tea" &
      cuisine_description != "Pancakes/Waffles" &
      cuisine_description != "Japanese" &
      cuisine_description != "Irish" &
      cuisine_description != "Indian" &
      cuisine_description != "Italian"
  ) %>%
  mutate(cuisine_description = fct_reorder(cuisine_description, score)) %>% 
  plot_ly(y = ~score, color = ~cuisine_description, type = "box")
```